Abstract
This paper deals with the optimal allocation (siting and sizing) of distributed electrical energy storage systems in unbalanced electrical distribution systems. This problem is formulated as a mixed, non-linear, constrained minimization problem, in which the objective function involves economic factors and constraints address the technical limitations of both network and distributed resources. The problem is cumbersome from the computational point of view due to the presence of both constraints of an intertemporal nature and a great number of state variables. In order to guarantee reasonable accuracy-although limiting the computational efforts-a new approach is proposed in this paper: it is based on a Simultaneous Perturbation Stochastic Approximation (SPSA) method and on an innovative inner algorithm, which allows it to quickly carry out the daily scheduling (charging/discharging) of the electrical energy storage systems. The proposed method is applied to a medium voltage (Institute of Electrical and Electronics Engineers) IEEE unbalanced test network, to demonstrate the effectiveness of the procedure in terms of computational effort while preserving the accuracy of the solution. The obtained results are also compared with the results of a Genetic Algorithm and of an exhaustive procedure.
Highlights
Electrical Energy Storage Systems (EESSs) have been recognized as a viable solution for implementing the smart grid paradigm, providing features in load levelling, integrating renewable and intermittent sources, improving power quality (PQ) and reliability, reducing energy import during peak demand periods, and so on [1].In particular, EESSs can be exploited in distribution systems to pursue several objectives that range from implementing demand response to minimizing electrical energy costs
EESSs are expensive and allocating the EESSs in a distribution system is crucial to minimizing the total investment cost while meeting system requirements
Taking into account the unbalanced nature of the distribution systems that requires a three-phase modeling of all of the components, the planning problem of allocating EESSs eventually consists in solving a high-dimensional mixed integer, non-linear optimization problem
Summary
Perturbation Stochastic Approximation Method for the Optimal Allocation of Electrical Energy. Dipartimento di Ingegneria Elettrica e dell0 Informazione “Maurizio Scarano”, Università di Cassino e del
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